Identifying conservation priorities for an understudied species in decline: Golden cats (Catopuma temminckii) in mainland Tropical Asia

Abstract Identifying conservation priorities for an understudied species can be challenging, as the amount and type of data available to work with are often limited. Here, we demonstrate a flexible workflow for identifying priorities for such data-limited species, focusing on the little-studied Asian golden cat (Catopuma temminckii) in mainland Tropical Asia. Using recent occurrence records, we modeled the golden cat's expected area of occurrence and identified remaining habitat strongholds (i.e., large intact areas with moderate-to-high expected occurrence). We then classified these strongholds by recent camera-trap survey status (from a literature review) and near-future threat status (based on publicly available forest loss projections and Bayesian Belief Network derived estimates of hunting-induced extirpation risk) to identify conservation priorities. Finally, we projected the species' expected area of occurrence in the year 2000, approximately three generations prior to today, to define past declines and better evaluate the species' current conservation status. Lower levels of hunting-induced extirpation risk and higher levels of closed-canopy forest cover were the strongest predictors of recent camera-trap records. Our projections suggest a 68% decline in area with moderate-to-high expected occurrence between 2000 and 2020, with a further 18% decline predicted over the next 20 years. Past and near-future declines were primarily driven by cumulatively increasing levels of hunting-induced extirpation risk, suggesting assessments of conservation status based solely on declines in habitat may underestimate actual population declines. Of the 40 remaining habitat strongholds, 77.5% were seriously threatened by forest loss and hunting. Only 52% of threatened strongholds had at least one site surveyed, compared to 100% of low-to-moderate threat strongholds, thus highlighting an important knowledge gap concerning the species' current distribution and population status. Our results suggest the golden cat has experienced, and will likely continue to experience, considerable population declines and should be considered for up-listing to a threatened category (i.e., VU/EN) under criteria A2c of the IUCN Red List

CamTrapAsia: a dataset of tropical forest vertebrate communities from 239 camera trapping studies

Information on tropical Asian vertebrates has traditionally been sparse, particularly when it comes to cryptic species inhabiting the dense forests of the region. Vertebrate populations are declining globally due to land-use change and hunting, the latter frequently referred as “defaunation.” This is especially true in tropical Asia where there is extensive land-use change and high human densities. Robust monitoring requires that large volumes of vertebrate population data be made available for use by the scientific and applied communities. Camera traps have emerged as an effective, non-invasive, widespread, and common approach to surveying vertebrates in their natural habitats. However, camera-derived datasets remain scattered across a wide array of sources, including published scientific literature, gray literature, and unpublished works, making it challenging for researchers to harness the full potential of cameras for ecology, conservation, and management. In response, we collated and standardized observations from 239 camera trap studies conducted in tropical Asia. There were 278,260 independent records of 371 distinct species, comprising 232 mammals, 132 birds, and seven reptiles. The total trapping effort accumulated in this data paper consisted of 876,606 trap nights, distributed among Indonesia, Singapore, Malaysia, Bhutan, Thailand, Myanmar, Cambodia, Laos, Vietnam, Nepal, and far eastern India. The relatively standardized deployment methods in the region provide a consistent, reliable, and rich count data set relative to other large-scale pressence-only data sets, such as the Global Biodiversity Information Facility (GBIF) or citizen science repositories (e.g., iNaturalist), and is thus most similar to eBird. To facilitate the use of these data, we also provide mammalian species trait information and 13 environmental covariates calculated at three spatial scales around the camera survey centroids (within 10-, 20-, and 30-km buffers). We will update the dataset to include broader coverage of temperate Asia and add newer surveys and covariates as they become available. This dataset unlocks immense opportunities for single-species ecological or conservation studies as well as applied ecology, community ecology, and macroecology investigations. The data are fully available to the public for utilization and research. Please cite this data paper when utilizing the data

CamTrapAsia: A dataset of tropical forest vertebrate communities from 239 camera trapping studies

Information on tropical Asian vertebrates has traditionally been sparse, particularly when it comes to cryptic species inhabiting the dense forests of the region. Vertebrate populations are declining globally due to land‐use change and hunting, the latter frequently referred as “defaunation.” This is especially true in tropical Asia where there is extensive land‐use change and high human densities. Robust monitoring requires that large volumes of vertebrate population data be made available for use by the scientific and applied communities. Camera traps have emerged as an effective, non‐invasive, widespread, and common approach to surveying vertebrates in their natural habitats. However, camera‐derived datasets remain scattered across a wide array of sources, including published scientific literature, gray literature, and unpublished works, making it challenging for researchers to harness the full potential of cameras for ecology, conservation, and management. In response, we collated and standardized observations from 239 camera trap studies conducted in tropical Asia. There were 278,260 independent records of 371 distinct species, comprising 232 mammals, 132 birds, and seven reptiles. The total trapping effort accumulated in this data paper consisted of 876,606 trap nights, distributed among Indonesia, Singapore, Malaysia, Bhutan, Thailand, Myanmar, Cambodia, Laos, Vietnam, Nepal, and far eastern India. The relatively standardized deployment methods in the region provide a consistent, reliable, and rich count data set relative to other large‐scale pressence‐only data sets, such as the Global Biodiversity Information Facility (GBIF) or citizen science repositories (e.g., iNaturalist), and is thus most similar to eBird. To facilitate the use of these data, we also provide mammalian species trait information and 13 environmental covariates calculated at three spatial scales around the camera survey centroids (within 10‐, 20‐, and 30‐km buffers). We will update the dataset to include broader coverage of temperate Asia and add newer surveys and covariates as they become available. This dataset unlocks immense opportunities for single‐species ecological or conservation studies as well as applied ecology, community ecology, and macroecology investigations. The data are fully available to the public for utilization and research. Please cite this data paper when utilizing the data

Diet analysis of Leopoldamys neilli, a cave-dwelling rodent in Southeast Asia, using Next-Generation Sequencing from feces

peer reviewedLeopoldamys neilli is a Murinae rodent endemic to limestone karst of Thailand and the Lao PDR, but its ecology and the reasons of its endemism to karst are still totally unknown. The aim of this pilot study was to examine the plant composition of the diet of L. neilli at the level of order and family using DNA for molecular identification and to compare it with two other forest-dwelling Leopoldamys species, L. herberti and L. sabanus. A 202bp fragment of the rbcL gene was amplified and sequenced for twenty-three fecal samples of the three species using 454 pyrosequencing. We successfully identified a total of seventeen orders and twenty-one plant families, corresponding to thirty-three putative species, in the feces of these three Leopoldamys species. Solanaceae were the most common plants in the diet of L.neilli regardless of the region and sampling season, and they were also present in feces of both L. herberti and L. sabanus. The Araceae, Fabaceae, and Apocynaceae families were also identified in feces of L. neilli collected in various regions of Thailand and at different seasons. Plants of the Oleaceae family are consumed by both L. herberti and L. sabanus but were not found in the diet of L. neilli. Further improvements of the study, such as the use of additional genes, the creation of a reference collection, the microhistological examination of plant fragments to determine which parts of the plant are consumed, and the analysis of the animal diet of Leopoldamys are suggested to enhance the quality and accuracy of the results obtained

Prior distribution of parameters used in our ABC analysis.

<p>Prior distribution of parameters used in our ABC analysis.</p

Sampling localities of the 225 <i>L. neilli</i> analyzed in this study.

<p>Sampling localities of the 225 <i>L. neilli</i> analyzed in this study.</p

Dating of the most recent common ancestors with 95% HPD (blue node bars) computed with BEAST and graphical representation of the biogeographic scenario of <i>L. neilli</i> according to four time periods (A, B, C, D) inferred by VIP, LAGRANGE and DIYABC.

<p>The four maps depict the hypothetical distribution of <i>L. neilli</i> ancestral population (dark grey) (obtained with LAGRANGE), western (mauve), central (yellow), northern (light blue) and northeastern (red) groups and the localisation of barriers (black lines) leading to three vicariant events (obtained with VIP).</p

Median-joining networks based on mitochondrial (A) and nuclear (B) datasets.

<p>Circles represent haplotypes obtained in this study and the circle size is proportional to the number of individuals sharing a haplotype. Squares represent median vectors. Numbers on branches represent the number of mutational steps between haplotypes.</p

Maximum likelihood tree based on mitochondrial dataset (GTR+G).

<p>Bootstrap support (1000 replicates) and posterior probabilities of nodes are indicated above and below the branches, respectively. Node support values from within lineages were removed for clarity.</p